湖南省森林碳平衡空间分异及对气候响应的模拟预测
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摘要
森林生态系统碳平衡是指在一定时间内特定区域的林分在单位时间和单位面积上的碳循环(GPP, NPP)以及森林与大气之间碳交换的净通量(NEP),即森林的生物碳固持效率,碳利用效率,以及森林生态系统碳固定输入与碳排放输出的平衡状况。它既对全球气候变化起着极其重要的作用,同时又对气候变化响应敏感。因此,对区域森林碳平衡的研究,不仅有利于加深对生态系统的物质循环和能量流动的认识和理解,而且有利于揭示生态系统对全球变化的响应规律及气候变化对生态系统的影响,具有重要意义。准确估算和预测区域森林碳平衡特征以及对未来气候变化的响应可以为科学预测气候变化、服务于减缓和适应气候变化的区域碳管理提供科学依据,是生态系统与全球变化科学研究的重要科技问题。TRIPLEX1.6(三元生态混合模型)是一个基于过程的混合模型,它减少了一些参数,全面但不复杂,成为可模拟森林生长和碳、氮动态变化的森林管理模型,用于制定森林管理决策(如生长和产量预测),量化森林碳储量以及对短期和长期的气候变化效应进行评价。本研究的目的为：(1)林分尺度上验证亚热带主要树种(杉木和马尾松)的碳循环及环境耦合机理,(2)中亚热带湖南省区域杉木和马尾松碳吸存及环境相关,(3)亚热带主要森林类型碳收支及气候响应机制,(3)亚热带主要森林类型碳收支及气候响应机制,(4)未来气候变化情景下中亚热带湖南省区域森林碳平衡及敏感性特征。
利用2000年1月至2009年1月的逐月气象数据,研究了湖南省区域主要树种,杉木和马尾松的碳贮量的现状及空间分异,建立由遥感数据驱动的湖南省内杉木马尾松碳储量在水热空间上的数学模型,揭示亚热带主要森林类型固碳潜力对气候变化的响应规律。表明TRIPLEX应用于杉木及马尾松模拟的由点到面的模拟是可行的。
在遥感技术和地理信息系统的支持下,以湖南省内主要的68个气象站点的月气温和降水信息、基于多CMIP5模式综合的对湖南省区域低(RCP4.5)和高(RCP8.5)两种排放浓度的未来50年气候变化预估值及该地区的数字高程数据(DEM)为基础,对气温、降水等气候因子进行了空间插值分析,为碳储量、生产力的估算和空间分析奠定基础。以卫星数据、地面气象数据、地形图、森林分布图和本区域的2004年湖南省一类调查中所用的2173个森林资源清查样地数据为主要模型初始化和立地参数化数据源。以文献收集、资料整理、实地测量等数据作为生理参数化的数据源。以独立于初始化和参数化以外的其他文献数据、样地调查数据、以及2009年湖南省一类森林资源清查样地数据进行模型对主要林分碳循环过程模拟能力的验证、湖南省内主要森林类型碳收支估算能力的验证、湖南省森林生态系统碳平衡的时空动态及气候响应的预测能力的验证。以最小像元为模拟单位,以多尺度信息、数据和技术融合的元数据分析框架,并引入林分、森林类型、区域森林等定性变量,通过相关分析、样地比对、栅格值Kappa比对等验证统计量进行林分、森林类型建模与区域森林总体建模的总体应用效果的检验。
主要结论如下：
1)验证相关性系数(r2)均在大与0.8以上,再将模拟值与调查值进行方差分析,和偏差检验,得到偏差(Bias%)均在10%以内,说明TRIPLEX用于湖南省四种森林类型的生长模拟是可以信任的。
2)未来50年(2010-2060年)湖南省区域平均气候变化的多模式集合预估结果表明：在RCP4.5和RCP8.5两种排放情景下,未来50年(2010-2060年)湖南省平均气温0.143℃/10a和0.52℃/10a,在RCP4.5和RCP8.5两种排放情景下,到2060年,湖南地区平均气温比2000-2009年的均值增加0.715和2.6℃。湖南省降水量在未来50年年际和全省区域变化不明显,局部差异增大这与他人预估结果基本相近。
3)空间上看,我省森林植被GPP、NPP、NEP分布呈现自东北平原湖区向中部丘陵,再向西南部区域逐渐增大,湖南省森林总初级生产力最高的区域主要集中西南山地区域。这主要是当前局部丘陵山地的湖南省地理资源和水热状况及认为干扰决定的。当前的湖南省森林GPP的格点数量分布为负偏态分布,说明湖南森林的固碳效率在未来还有一定的提升空间。
为TRIPLEX模型的参数化、关键过程的优化及校正、不同尺度数据的融合等技术的革新提供了科学依据,有效地降低模型模拟的不确定性,具有重要的实践意义。
Forest carbon balance refers to the carbon sequestrated in gross primary productivity (GPP), net primary productivity (NPP) and net ecosystem productivity (NEP) of forest ecosystem in unit time and unit area. It not only plays an important role in the global climate change, but also is susceptible to the climate change. Therefore, the study on the regional forest carbon balance would help to enhance the understanding of matter cycle and energy flowing of ecosystem and reveal the corresponding rules of ecosystem to the global climate change and impacts of global climate change to the ecosystem and their sensitivity to increases in atmosphere CO2concentration, changes in temperature and precipitation. Accurate estimation of forest carbon balance is important for understanding the dynamics of the terrestrial carbon resulting from human-induced climate change, and makes the foundation for land surface, ecological and hydrological modeling, carbon-and water-cycle studies, and research on global climate change. TRIPLEX is a process-based hybrid mode, it focuses on the physiological processes of C assimilation, C contrast with most existing models, also supports component overload, where two or more components that have the same or a similar role in the customized model, can work together. This further increases the flexibility of model reuse. TRIPLEX has the potential and flexibility to allow users to piece together a complete, customized forest simulation model from components that were built for other well-established models. The model could be expanded dynamically and used for research on climate change, forest carbon budgets and dynamics, forest succession, and ecosystem disturbances (e.g. harvest, fire, and insect outbreaks).
This study aims to produce accurate estimation of forest carbon balance and potential response to future climate change with process-based hybrid model TRIPLEX1.6in Hunan province, subtropical China. The objectives of this study were to:(1) at stand level, calibrate and validate TRIPLEX for applicability to large scale C. lanceolata and P. massoniana forests,(2) simulate C. lanceolata and P. massoniana forest stand production in Hunan Province, southern China, and make quantitative analysis of relationships between simulated forest stand growth on a provincial scale and spatial patterns of controlling factors;(3) regional validate TRIPLEX for applicabilities to four forest types;(4) predict long-term spatial-temporal patterns of forest carbon balance in Hunan province with the climate inputs from multi-CMIP5emsembled outputs and its responses to climate change, and make sensitive analysis of relationships between projected potential of forest carbon balance on multi-scale spatial-temporal impacts.
In the support of Remote Sensing and Geographic Information System, based on the meteorological records of temperature and precipitation data measured at68stations, year1956-2009; the multi-CMIP5model ensembled half-Century projections of future climate trends over Hunan province under the RCP4.5and RCP8.5scenario; and the DEM data of the Hunan province region, the raster of climate inputs were intepolated, which would be the environment inputs of the carbon storage and NPP. MODIS data with250m spatial resolution, climate data,30m DEM, forest map and2173permanent sample plot (PSP2004) and1973plots of PSP2009data of National Forest Inventory (NFI) were used to the carbon model. To get a more accurate model of estimating forest carbon storage, the forest was divided into four types (pine forest, PF; fir forest, FF; deciduous broadleaved forest, DBF; evergreen broadleaved forest, EBF).
Our results indicate that:By the use of monthly climate data from Jan.2000to Jan.2009at each stand level, the spatial pattern and dynamics of carbon storage for Cunninghamia lanceolata and Pinus massoniana forest stand production in Hunan Province, China. The process-based hybrid model is a promising tool for predicting forest stand roduction on regional scales. TRIPLEX1.6is capable in predicting forest growth and biomass dynamics of subtropical coniferous forests. Moreover, independent validations determined that TRIPLEX1.6demonstrated competence in extrapolating outcomes on regional scales as well as withstanding rigorous testing in predicting C storage in subtropical forest ecosystems.
net primary production (NPP) flux for atmospheric carbon dioxide has varied slightly from year-to-year, but was predicted to have increased over short multi-year periods in the regions of the forested area, and the western since the year2000. These results for were found to be in contrast to other recently published modeling trends for terrestrial NPP with high sensitivity to regional drying patterns. Nonetheless, periodic declines in regional NPP were predicted by TRIPPLEX for the southern and western, the southern Hunan, and southern and eastern Hunan. NPP in subtropical forest zones was examined in greater detail to discover lower annual production values than previously reported in many global models across the subtropical forest zones, likely due to the enhanced detection of lower production ecosystems replacing primary subtropical forest.
Nevertheless, TRIPLEX model validation is as yet incomplete. This is primarily the result of the absence of observed soil C data in the plots selected for this study. More rigorous testing of the ability of the model to simulate soil C, N, and water dynamics for various forest ecosystems is a priority in its ongoing development. There are, however, several sources of uncertainty associated with model estimates such as PSP measurement uncertainty, input data uncertainty, model structural uncertainty, and uncertainties pertaining to how strongly limited validation sources represent forest dynamics. Parameter values derived from different sources also contain substantial uncertainties as do impacts from natural and anthropogenic disturbances such as land use change, forest fires, insect-induced mortality and harvesting as well as uncertainties related to the techniques used to fill data gaps. However, despite these limitations, TRIPLEX simulation results fall into the reasonable bounds of measured and published values. Furthermore, additional modules related to the effects of CO2fertilization, ecosystem disturbances (e.g., fire, harvesting, insects, and disease), and soil water stress on forest growth and C and N dynamics must be included in the future development and application of TREPLEX.
利用2000年1月至2009年1月的逐月气象数据,研究了湖南省区域主要树种,杉木和马尾松的碳贮量的现状及空间分异,建立由遥感数据驱动的湖南省内杉木马尾松碳储量在水热空间上的数学模型,揭示亚热带主要森林类型固碳潜力对气候变化的响应规律。表明TRIPLEX应用于杉木及马尾松模拟的由点到面的模拟是可行的。
在遥感技术和地理信息系统的支持下,以湖南省内主要的68个气象站点的月气温和降水信息、基于多CMIP5模式综合的对湖南省区域低(RCP4.5)和高(RCP8.5)两种排放浓度的未来50年气候变化预估值及该地区的数字高程数据(DEM)为基础,对气温、降水等气候因子进行了空间插值分析,为碳储量、生产力的估算和空间分析奠定基础。以卫星数据、地面气象数据、地形图、森林分布图和本区域的2004年湖南省一类调查中所用的2173个森林资源清查样地数据为主要模型初始化和立地参数化数据源。以文献收集、资料整理、实地测量等数据作为生理参数化的数据源。以独立于初始化和参数化以外的其他文献数据、样地调查数据、以及2009年湖南省一类森林资源清查样地数据进行模型对主要林分碳循环过程模拟能力的验证、湖南省内主要森林类型碳收支估算能力的验证、湖南省森林生态系统碳平衡的时空动态及气候响应的预测能力的验证。以最小像元为模拟单位,以多尺度信息、数据和技术融合的元数据分析框架,并引入林分、森林类型、区域森林等定性变量,通过相关分析、样地比对、栅格值Kappa比对等验证统计量进行林分、森林类型建模与区域森林总体建模的总体应用效果的检验。
主要结论如下：
1)验证相关性系数(r2)均在大与0.8以上,再将模拟值与调查值进行方差分析,和偏差检验,得到偏差(Bias%)均在10%以内,说明TRIPLEX用于湖南省四种森林类型的生长模拟是可以信任的。
2)未来50年(2010-2060年)湖南省区域平均气候变化的多模式集合预估结果表明：在RCP4.5和RCP8.5两种排放情景下,未来50年(2010-2060年)湖南省平均气温0.143℃/10a和0.52℃/10a,在RCP4.5和RCP8.5两种排放情景下,到2060年,湖南地区平均气温比2000-2009年的均值增加0.715和2.6℃。湖南省降水量在未来50年年际和全省区域变化不明显,局部差异增大这与他人预估结果基本相近。
3)空间上看,我省森林植被GPP、NPP、NEP分布呈现自东北平原湖区向中部丘陵,再向西南部区域逐渐增大,湖南省森林总初级生产力最高的区域主要集中西南山地区域。这主要是当前局部丘陵山地的湖南省地理资源和水热状况及认为干扰决定的。当前的湖南省森林GPP的格点数量分布为负偏态分布,说明湖南森林的固碳效率在未来还有一定的提升空间。
为TRIPLEX模型的参数化、关键过程的优化及校正、不同尺度数据的融合等技术的革新提供了科学依据,有效地降低模型模拟的不确定性,具有重要的实践意义。
Forest carbon balance refers to the carbon sequestrated in gross primary productivity (GPP), net primary productivity (NPP) and net ecosystem productivity (NEP) of forest ecosystem in unit time and unit area. It not only plays an important role in the global climate change, but also is susceptible to the climate change. Therefore, the study on the regional forest carbon balance would help to enhance the understanding of matter cycle and energy flowing of ecosystem and reveal the corresponding rules of ecosystem to the global climate change and impacts of global climate change to the ecosystem and their sensitivity to increases in atmosphere CO2concentration, changes in temperature and precipitation. Accurate estimation of forest carbon balance is important for understanding the dynamics of the terrestrial carbon resulting from human-induced climate change, and makes the foundation for land surface, ecological and hydrological modeling, carbon-and water-cycle studies, and research on global climate change. TRIPLEX is a process-based hybrid mode, it focuses on the physiological processes of C assimilation, C contrast with most existing models, also supports component overload, where two or more components that have the same or a similar role in the customized model, can work together. This further increases the flexibility of model reuse. TRIPLEX has the potential and flexibility to allow users to piece together a complete, customized forest simulation model from components that were built for other well-established models. The model could be expanded dynamically and used for research on climate change, forest carbon budgets and dynamics, forest succession, and ecosystem disturbances (e.g. harvest, fire, and insect outbreaks).
This study aims to produce accurate estimation of forest carbon balance and potential response to future climate change with process-based hybrid model TRIPLEX1.6in Hunan province, subtropical China. The objectives of this study were to:(1) at stand level, calibrate and validate TRIPLEX for applicability to large scale C. lanceolata and P. massoniana forests,(2) simulate C. lanceolata and P. massoniana forest stand production in Hunan Province, southern China, and make quantitative analysis of relationships between simulated forest stand growth on a provincial scale and spatial patterns of controlling factors;(3) regional validate TRIPLEX for applicabilities to four forest types;(4) predict long-term spatial-temporal patterns of forest carbon balance in Hunan province with the climate inputs from multi-CMIP5emsembled outputs and its responses to climate change, and make sensitive analysis of relationships between projected potential of forest carbon balance on multi-scale spatial-temporal impacts.
In the support of Remote Sensing and Geographic Information System, based on the meteorological records of temperature and precipitation data measured at68stations, year1956-2009; the multi-CMIP5model ensembled half-Century projections of future climate trends over Hunan province under the RCP4.5and RCP8.5scenario; and the DEM data of the Hunan province region, the raster of climate inputs were intepolated, which would be the environment inputs of the carbon storage and NPP. MODIS data with250m spatial resolution, climate data,30m DEM, forest map and2173permanent sample plot (PSP2004) and1973plots of PSP2009data of National Forest Inventory (NFI) were used to the carbon model. To get a more accurate model of estimating forest carbon storage, the forest was divided into four types (pine forest, PF; fir forest, FF; deciduous broadleaved forest, DBF; evergreen broadleaved forest, EBF).
Our results indicate that:By the use of monthly climate data from Jan.2000to Jan.2009at each stand level, the spatial pattern and dynamics of carbon storage for Cunninghamia lanceolata and Pinus massoniana forest stand production in Hunan Province, China. The process-based hybrid model is a promising tool for predicting forest stand roduction on regional scales. TRIPLEX1.6is capable in predicting forest growth and biomass dynamics of subtropical coniferous forests. Moreover, independent validations determined that TRIPLEX1.6demonstrated competence in extrapolating outcomes on regional scales as well as withstanding rigorous testing in predicting C storage in subtropical forest ecosystems.
net primary production (NPP) flux for atmospheric carbon dioxide has varied slightly from year-to-year, but was predicted to have increased over short multi-year periods in the regions of the forested area, and the western since the year2000. These results for were found to be in contrast to other recently published modeling trends for terrestrial NPP with high sensitivity to regional drying patterns. Nonetheless, periodic declines in regional NPP were predicted by TRIPPLEX for the southern and western, the southern Hunan, and southern and eastern Hunan. NPP in subtropical forest zones was examined in greater detail to discover lower annual production values than previously reported in many global models across the subtropical forest zones, likely due to the enhanced detection of lower production ecosystems replacing primary subtropical forest.
Nevertheless, TRIPLEX model validation is as yet incomplete. This is primarily the result of the absence of observed soil C data in the plots selected for this study. More rigorous testing of the ability of the model to simulate soil C, N, and water dynamics for various forest ecosystems is a priority in its ongoing development. There are, however, several sources of uncertainty associated with model estimates such as PSP measurement uncertainty, input data uncertainty, model structural uncertainty, and uncertainties pertaining to how strongly limited validation sources represent forest dynamics. Parameter values derived from different sources also contain substantial uncertainties as do impacts from natural and anthropogenic disturbances such as land use change, forest fires, insect-induced mortality and harvesting as well as uncertainties related to the techniques used to fill data gaps. However, despite these limitations, TRIPLEX simulation results fall into the reasonable bounds of measured and published values. Furthermore, additional modules related to the effects of CO2fertilization, ecosystem disturbances (e.g., fire, harvesting, insects, and disease), and soil water stress on forest growth and C and N dynamics must be included in the future development and application of TREPLEX.
引文
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